{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据 处理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集\n",
    "train_fasion_mnist = tfds.as_numpy(tfds.load(\"fashion_mnist\",split=\"train\",data_dir=\"./\",download=False,batch_size=-1))\n",
    "x_train , y_train = train_fasion_mnist[\"image\"],train_fasion_mnist[\"label\"]\n",
    "#测试集\n",
    "test_fasion_mnist = tfds.as_numpy(tfds.load(\"fashion_mnist\",split=\"test\",data_dir=\"./\",download=False,batch_size=-1))\n",
    "x_test, y_test = test_fasion_mnist[\"image\"],test_fasion_mnist[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Samples: 60000\n",
      "Test Samples: 60000\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Samples:\",len(x_train))\n",
    "print(\"Test Samples:\",len(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = x_train.copy()\n",
    "x2 = x_test.copy()\n",
    "y1 = y_train.copy()\n",
    "y2 = y_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (10000, 28, 28, 1) (60000,) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x1.shape,x2.shape,y1.shape,y2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.squeeze(x1,axis=-1)\n",
    "x2= np.squeeze(x2,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(x1.shape,x2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制一张随机样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "arr = np.random.randint(1,60000)\n",
    "plt.imshow(x1[arr])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_classes = {0:'T恤',1:'裤子',2:'套衫',3:'裙子',4:'外套',5:'凉鞋',6:'汗衫',7:'运动鞋',8:'包',9:'踝靴'}\n",
    "mnist_classes = [i for i in range(10)]\n",
    "num_classes = 10\n",
    "batch_size = 500\n",
    "num_epochs = 80\n",
    "iterations = 3\n",
    "nb_augmentation = 2\n",
    "img_width = 28\n",
    "img_height = 28\n",
    "channels = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x1 = x1/255.0\n",
    "#x2 = x2/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']='SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x1[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(fashion_classes[y1[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据增强\n",
    "深层神经网络一般都需要大量的训练数据才能获得比较理想的结果。在数据量有限的情况下，可以通过数据增强（Data Augmentation）来增加训练样本的多样性， 提高模型鲁棒性，避免过拟合.在计算机视觉中，典型的数据增强方法有翻转（Flip），旋转（Rotat ），缩放（Scale），随机裁剪或补零（Random Crop or Pad），色彩抖动（Color jittering），加噪声（Noise）等。\n",
    "本案例进行了旋转、反转和填充模式的增强。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "#定义用于图像增强的函数\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=10,        #旋转角度\n",
    "#    width_shift_range=0.2,   #水平偏移\n",
    "#    height_shift_range=0.2,  #垂直偏移\n",
    "#    shear_range=0.2,         #随机错切变换的角度\n",
    "#    zoom_range=0.2,          #随机缩放的范围\n",
    "    horizontal_flip=True,    #随机将一半图像水平翻转\n",
    "    fill_mode='nearest'      #随机将一半图像水平翻转\n",
    ")\n",
    "#image 原始图像\n",
    "#nb_augmentation 增加的数量\n",
    "#images 初始化后的图像\n",
    "\n",
    "def image_augmentation(image, nb_of_augmentation):\n",
    "    images = []\n",
    "    image = image.reshape(1,img_height,img_width,channels)\n",
    "    i = 0\n",
    "    for x_batch in datagen.flow(image,batch_size=1):\n",
    "        images.append(x_batch)\n",
    "        i += 1\n",
    "        if i >= nb_of_augmentation:\n",
    "            break\n",
    "    return images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "原始图像处理:\n",
    "将像素缩放为0.0-1.0之间\n",
    "添加增强的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "#targets 目标\n",
    "#use_sugmentation 进行数据增强则设置为True\n",
    "#nb_of_augmentation 图像增强设置的数量\n",
    "\n",
    "def preprocess_data(images, targets, use_augmentation=False, nb_of_augmentation=1):\n",
    "    \n",
    "    X = []\n",
    "    y = []\n",
    "    for x_, y_ in zip(images, targets):\n",
    "        #像素缩放\n",
    "        x_ = x_ / 255.0\n",
    "        #数据增强\n",
    "        if use_augmentation:\n",
    "            argu_img= image_augmentation(x_, nb_of_augmentation)\n",
    "            for a in argu_img:\n",
    "                X.append(a.reshape(img_height, img_width, channels))\n",
    "                y.append(y_)\n",
    "                \n",
    "        X.append(x_)\n",
    "        y.append(y_)\n",
    "    return np.array(X),tf.keras.utils.to_categorical(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用上面定义的函数进行数据的预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_shaped, y_train_shaped = preprocess_data(\n",
    "    x_train, y_train,\n",
    "    use_augmentation = True,\n",
    "    nb_of_augmentation = nb_augmentation\n",
    ")\n",
    "X_test_shaped, y_test_shaped = preprocess_data(x2,y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization (BatchNo (None, 28, 28, 1)         4         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 28, 28, 64)        1088      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        65600     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               409856    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 493,902\n",
      "Trainable params: 493,772\n",
      "Non-trainable params: 130\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    #创建简单序贯模型\n",
    "    #使用keras的squential搭建神经网络模型时使用add进行一层一层的搭建\n",
    "    cnn = tf.keras.Sequential()\n",
    "    #添加新入层\n",
    "    cnn.add(tf.keras.layers.InputLayer(input_shape=(img_height,img_width,channels)))\n",
    "    # BatchNormaLization(BN)可以防止梯度爆炸或猕散、可以提高训练时模型对于不同超参（学习率、初始化)的鲁棒性\n",
    "    #可以让大部分的激活N娄育总够远离其饱和区域\n",
    "    cnn.add(tf.keras.layers.BatchNormalization())\n",
    "    #卷积层和池化层\n",
    "    cnn.add(tf.keras.layers.Convolution2D(64,(4,4),padding= 'same', activation= 'relu'))\n",
    "    cnn.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
    "    #Dropout\n",
    "    #是种防上过以合的手段\n",
    "    cnn.add(tf.keras.layers.Dropout(0.1))#卷积层利和池化层\n",
    "    cnn.add(tf.keras.layers.Convolution2D(64,(4,4),activation='relu'))\n",
    "    cnn.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
    "    # Dropout\n",
    "    cnn.add(tf.keras.layers.Dropout(0.3))\n",
    "    #维特征向量转换维维特征向量\n",
    "    cnn.add(tf.keras.layers.Flatten())\n",
    "    #全连接层                                \n",
    "    #激活擞采用relu\n",
    "    cnn.add(tf.keras.layers.Dense(256,activation='relu'))\n",
    "    #Dropout\n",
    "    cnn.add(tf.keras.layers.Dropout(0.5))\n",
    "    #全连接层\n",
    "    cnn.add(tf.keras.layers.Dense(64,activation='relu'))\n",
    "    #Normalization\n",
    "    cnn.add(tf.keras.layers.BatchNormalization())\n",
    "    #激活国数采用softmax\n",
    "    #softmax被用于多分类问题\n",
    "    cnn.add(tf.keras.layers.Dense(num_classes, activation='softmax' ))\n",
    "    #相当于一个汇总，损失函数采用交叉嫡，优化器选择adam，评估指标选用准确率\n",
    "    cnn.compile(loss='categorical_crossentropy',optimizer=tf.keras.optimizers.Adam(),metrics=['accuracy'])\n",
    "    return cnn\n",
    "create_model().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练执行\n",
    "上面的函数定义了模型的结构，下面使用定义的结构来进行模型的训练。使用fit函数来进行模型的训练。\n",
    "首先需要划分训练集和测试集，使用sklearn的train_test_split函数来进行数据的划分，划分比例为5：1训练集和测试集划分完成，则进行模型的训练，并将每个iter的最佳模型保存为hdf5格式的checkpoint，以便后期模型的调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration: 0\n",
      "Epoch 1/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.5951 - accuracy: 0.7895\n",
      "Epoch 00001: val_loss improved from inf to 0.52167, saving model to fashion_mnist-0.hdf5\n",
      "288/288 [==============================] - 79s 273ms/step - loss: 0.5951 - accuracy: 0.7895 - val_loss: 0.5217 - val_accuracy: 0.8690\n",
      "Epoch 2/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.3609 - accuracy: 0.8696\n",
      "Epoch 00002: val_loss improved from 0.52167 to 0.28176, saving model to fashion_mnist-0.hdf5\n",
      "288/288 [==============================] - 79s 274ms/step - loss: 0.3609 - accuracy: 0.8696 - val_loss: 0.2818 - val_accuracy: 0.8972\n",
      "iteration: 1\n",
      "Epoch 1/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.5955 - accuracy: 0.7883\n",
      "Epoch 00001: val_loss improved from inf to 0.51475, saving model to fashion_mnist-1.hdf5\n",
      "288/288 [==============================] - 80s 277ms/step - loss: 0.5955 - accuracy: 0.7883 - val_loss: 0.5147 - val_accuracy: 0.8764\n",
      "Epoch 2/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.3580 - accuracy: 0.8701\n",
      "Epoch 00002: val_loss improved from 0.51475 to 0.27005, saving model to fashion_mnist-1.hdf5\n",
      "288/288 [==============================] - 79s 275ms/step - loss: 0.3580 - accuracy: 0.8701 - val_loss: 0.2701 - val_accuracy: 0.9009\n",
      "iteration: 2\n",
      "Epoch 1/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.5928 - accuracy: 0.7885\n",
      "Epoch 00001: val_loss improved from inf to 0.54828, saving model to fashion_mnist-2.hdf5\n",
      "288/288 [==============================] - 79s 274ms/step - loss: 0.5928 - accuracy: 0.7885 - val_loss: 0.5483 - val_accuracy: 0.8742\n",
      "Epoch 2/2\n",
      "288/288 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8695\n",
      "Epoch 00002: val_loss improved from 0.54828 to 0.26879, saving model to fashion_mnist-2.hdf5\n",
      "288/288 [==============================] - 79s 273ms/step - loss: 0.3585 - accuracy: 0.8695 - val_loss: 0.2688 - val_accuracy: 0.9008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "histories = []\n",
    "for i in range(0,iterations):\n",
    "    print('iteration: %i' %i)\n",
    "    filepath = \"fashion_mnist-%i.hdf5\" %i\n",
    "    X_train_, x_val_, y_train_, y_val_ = train_test_split(X_train_shaped, y_train_shaped,test_size=0.2, random_state=42)\n",
    "    cnn = create_model()\n",
    "    history = cnn.fit(X_train_,y_train_,\n",
    "                      batch_size=batch_size,\n",
    "                      epochs=2,#num epochs,\n",
    "                      verbose=1,\n",
    "                      validation_data=(x_val_,y_val_),\n",
    "                      callbacks=[tf.keras.callbacks.ModelCheckpoint(filepath,monitor= 'val_loss',verbose=1,save_best_only=True)])\n",
    "    histories.append(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以最小损失值作为标准来进行模型的保存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集: \t0.35914702 loss / 0.86976157 acc\n",
      "验证集: \t0.27353455 loss / 0.89961110 acc\n"
     ]
    }
   ],
   "source": [
    "def get_avg(histories,his_key):\n",
    "    tmp = []\n",
    "    for history in histories:\n",
    "        tmp.append(history[his_key][np.argmin(history['val_loss'])])\n",
    "    return np.mean(tmp)\n",
    "print('训练集: \\t%0.8f loss / %0.8f acc' % (get_avg(histories,'loss'),get_avg(histories,'accuracy')))\n",
    "print('验证集: \\t%0.8f loss / %0.8f acc' % (get_avg(histories,'val_loss'),get_avg(histories,'val_accuracy')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 确定所有模型的损失和准确性。测试集上所有模型的损失和准确度  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "SavedModel file does not exist at: models/fashion_mnist-0.hdf5/{saved_model.pbtxt|saved_model.pb}",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-baee5d87751d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miterations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mcnn_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"models/fashion_mnist-%i.hdf5\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcnn_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test_shaped\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_test_shaped\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mverbose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtest_loss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\save.py\u001b[0m in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[0;32m    184\u001b[0m     \u001b[0mfilepath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpath_to_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m       \u001b[0mloader_impl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m    111\u001b[0m                   (export_dir,\n\u001b[0;32m    112\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 113\u001b[1;33m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: SavedModel file does not exist at: models/fashion_mnist-0.hdf5/{saved_model.pbtxt|saved_model.pb}"
     ]
    }
   ],
   "source": [
    "test_loss = []\n",
    "test_acc = []\n",
    "for i in range(0,iterations):\n",
    "    cnn_ = tf.keras.models.load_model(\"models/fashion_mnist-%i.hdf5\" % i)\n",
    "    score = cnn_.evaluate(X_test_shaped,y_test_shaped,verbose = 0)\n",
    "    test_loss.append(score[0])\n",
    "    test_accs.append(score[1])\n",
    "    print('Running final test with model %i:%0.4f loss/%0.4f acc' % (i,score[0],score[1]))\n",
    "print('\\nAverage loss / accuracy on testset:%0.4f loss / %0.5f acc' % (np.mean(test_loss),np.mean(test_accs)))\n",
    "print('Standard deviation: (+-%0.4f) loss / (+-%0.4f)acc' % (np.std(text_loss),np.std(test_accs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制每个iter的准确性和损失曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_acc_loss(title,histories,key_acc,key_loss):\n",
    "    fig,(ax1,ax2) = plt.subplots(1,2)\n",
    "    #Accuracy\n",
    "    ax1.set_title('Model accuracy - %s' % title)\n",
    "    names = []\n",
    "    for i,model in enumerate(histories):            \n",
    "        ax1.plot(model[key_acc])\n",
    "        ax1.set_xlabel('epoch')\n",
    "        names.append('Model %i' % i)\n",
    "        ax1.set_ylabel('accuracy')\n",
    "    ax1.legend(names,loc='upper left')\n",
    "    #Loss\n",
    "    ax2.set_title('Model loss - %s' % title)\n",
    "    for model in histories:        \n",
    "        ax2.plot(model[key_loss])\n",
    "        ax2.set_xlabel('epoch')\n",
    "        ax2.set_ylabel('loss')\n",
    "    ax2.legend(names,loc='upper left')\n",
    "    fig.set_size_inches(20,5)\n",
    "    plt.show()\n",
    "plot_acc_loss('training',histories,'accuracy','loss')\n",
    "plot_acc_loss('validation',histories,'val_accuracy','val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy.random' has no attribute 'randiant'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-cc7fbeb957dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     21\u001b[0m             \u001b[0mclass_\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mplot_sample_predictions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfashion_classes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test_shaped\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-33-cc7fbeb957dd>\u001b[0m in \u001b[0;36mplot_sample_predictions\u001b[1;34m(classes, model, X_test, y_test)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0melements\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m \u001b[1;33m==\u001b[0m\u001b[0mclass_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m             \u001b[0mrandom\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandiant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melements\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0melements\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0melements\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'numpy.random' has no attribute 'randiant'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False#用来正常显示负号\n",
    "def plot_sample_predictions(classes,model,X_test,y_test):\n",
    "    class_ = 0\n",
    "    images_per_row = 5\n",
    "    rows = len(classes) // images_per_row\n",
    "    for i in range(rows):\n",
    "        fig, axis = plt.subplots(1, images_per_row)\n",
    "        for i, axis in enumerate(axis):\n",
    "            elements = np.squeeze(np.argwhere(y_test ==class_))\n",
    "            random = np.random.randiant(len(elements))\n",
    "            X = X_test[elements[random]]\n",
    "            y = y_test[elements[random]]\n",
    "            fig.set_size_inches(10,20)\n",
    "            x_reshape = X.reshape([1,img_height,img_width,channels])\n",
    "            axis.text(0,32,'Predicted:{}'.format(classes[np.argmax(model.predict(x_reshape))]))\n",
    "            axis.text(0,36,'Correct:{}'.format((classes)[y]))\n",
    "            axis.imshow(np.squeeze(X),cmp='gray')\n",
    "            axis.axis('off')\n",
    "            class_ += 1\n",
    "    plt.show()\n",
    "plot_sample_predictions(list(fashion_classes.values()),model, X_test_shaped, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示随机样本的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
